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ORCIDDaniel Sheldon
Dan Sheldon – Daniel R. Sheldon
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- affiliation: University of Massachusetts Amherst, College of Information and Computer Sciences, MA, USA
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2020 – today
- 2024
[c52]Gustavo Pérez, Subhransu Maji, Daniel Sheldon:
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling. AAAI 2024: 22294-22302- [i35]Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. CoRR abs/2403.07797 (2024) - 2023
- [j11]Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. Trans. Mach. Learn. Res. 2023 (2023) - [c51]Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon:
Automatically marginalized MCMC in probabilistic programming. ICML 2023: 18301-18318 - [i34]Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon:
Automatically Marginalized MCMC in Probabilistic Programming. CoRR abs/2302.00564 (2023) - [i33]Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. CoRR abs/2302.13918 (2023) - [i32]Javier Burroni, Justin Domke, Daniel Sheldon:
Sample Average Approximation for Black-Box VI. CoRR abs/2304.06803 (2023) - [i31]Mohit Yadav, Daniel Sheldon, Cameron Musco:
Kernel Interpolation with Sparse Grids. CoRR abs/2305.14451 (2023) - [i30]Gustavo Pérez, Subhransu Maji, Daniel Sheldon:
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling. CoRR abs/2306.03151 (2023) - [i29]Gustavo Pérez, Daniel Sheldon, Grant Van Horn, Subhransu Maji:
Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling. CoRR abs/2312.05287 (2023) - 2022
- [j10]Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau:
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. Proc. VLDB Endow. 15(11): 2599-2612 (2022) - [c50]Jinlin Lai, Justin Domke, Daniel Sheldon:
Variational Marginal Particle Filters. AISTATS 2022: 875-895 - [c49]Cecilia Ferrando, Shufan Wang, Daniel Sheldon:
Parametric Bootstrap for Differentially Private Confidence Intervals. AISTATS 2022: 1598-1618 - [c48]Mohit Yadav, Daniel R. Sheldon, Cameron Musco:
Kernel Interpolation with Sparse Grids. NeurIPS 2022 - [i28]Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau:
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. CoRR abs/2201.12677 (2022) - 2021
- [j9]Zezhou Cheng, Subhransu Maji, Daniel Sheldon:
AI for conservation: learning to track birds with radar. XRDS 27(4): 26-29 (2021) - [j8]Ryan McKenna, Gerome Miklau
, Daniel Sheldon:
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. J. Priv. Confidentiality 11(3) (2021) - [c47]Mohit Yadav, Daniel Sheldon, Cameron Musco:
Faster Kernel Interpolation for Gaussian Processes. AISTATS 2021: 2971-2979 - [c46]Cheng Gu, Erik G. Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau:
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data. ICCV 2021: 13475-13484 - [c45]Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau:
Relaxed Marginal Consistency for Differentially Private Query Answering. NeurIPS 2021: 20696-20707 - [c44]Shiv Shankar, Daniel Sheldon:
Sibling Regression for Generalized Linear Models. ECML/PKDD (2) 2021: 781-795 - [i27]Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich:
Three-quarter Sibling Regression for Denoising Observational Data. CoRR abs/2101.00074 (2021) - [i26]Mohit Yadav, Daniel Sheldon, Cameron Musco:
Faster Kernel Interpolation for Gaussian Processes. CoRR abs/2101.11751 (2021) - [i25]Cheng Gu, Erik G. Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau:
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data. CoRR abs/2106.06887 (2021) - [i24]Shiv Shankar, Daniel Sheldon:
Sibling Regression for Generalized Linear Models. CoRR abs/2107.01338 (2021) - [i23]Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. CoRR abs/2108.04978 (2021) - [i22]Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau:
Relaxed Marginal Consistency for Differentially Private Query Answering. CoRR abs/2109.06153 (2021) - [i21]Jinlin Lai, Daniel Sheldon, Justin Domke:
Variational Marginal Particle Filters. CoRR abs/2109.15134 (2021) - 2020
- [c43]Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler:
Detecting and Tracking Communal Bird Roosts in Weather Radar Data. AAAI 2020: 378-385 - [c42]Abhinav Agrawal, Daniel Sheldon, Justin Domke:
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. NeurIPS 2020 - [c41]Ryan McKenna, Daniel Sheldon:
Permute-and-Flip: A new mechanism for differentially private selection. NeurIPS 2020 - [i20]Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler:
Detecting and Tracking Communal Bird Roosts in Weather Radar Data. CoRR abs/2004.12819 (2020) - [i19]Cecilia Ferrando, Shufan Wang, Daniel Sheldon:
General-Purpose Differentially-Private Confidence Intervals. CoRR abs/2006.07749 (2020) - [i18]Abhinav Agrawal, Daniel Sheldon, Justin Domke:
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. CoRR abs/2006.10343 (2020) - [i17]Edmond Cunningham, Renos Zabounidis, Abhinav Agrawal, Ina Fiterau, Daniel Sheldon:
Normalizing Flows Across Dimensions. CoRR abs/2006.13070 (2020) - [i16]Ryan McKenna, Daniel Sheldon:
Permute-and-Flip: A new mechanism for differentially private selection. CoRR abs/2010.12603 (2020)
2010 – 2019
- 2019
- [j7]Carla P. Gomes, Thomas G. Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Z. Fern, Daniel Fink, Douglas H. Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John M. Gregoire, John E. Hopcroft, Steve Kelling, J. Zico Kolter, Warren B. Powell, Nicole D. Sintov, John S. Selker, Bart Selman, Daniel Sheldon, David B. Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman:
Computational sustainability: computing for a better world and a sustainable future. Commun. ACM 62(9): 56-65 (2019) - [c40]Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon:
A Bayesian Perspective on the Deep Image Prior. CVPR 2019: 5443-5451 - [c39]Ryan McKenna, Daniel Sheldon, Gerome Miklau:
Graphical-model based estimation and inference for differential privacy. ICML 2019: 4435-4444 - [c38]Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich:
Three-quarter Sibling Regression for Denoising Observational Data. IJCAI 2019: 5960-5966 - [c37]Justin Domke, Daniel Sheldon:
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. NeurIPS 2019: 338-347 - [c36]Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Linear Regression. NeurIPS 2019: 523-533 - [i15]Ryan McKenna, Daniel Sheldon, Gerome Miklau:
Graphical-model based estimation and inference for differential privacy. CoRR abs/1901.09136 (2019) - [i14]Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon:
A Bayesian Perspective on the Deep Image Prior. CoRR abs/1904.07457 (2019) - [i13]Justin Domke, Daniel Sheldon:
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. CoRR abs/1906.10115 (2019) - [i12]Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Linear Regression. CoRR abs/1910.13153 (2019) - 2018
- [c35]Srinivasan Iyengar, Stephen Lee, Daniel Sheldon, Prashant J. Shenoy:
SolarClique: Detecting Anomalies in Residential Solar Arrays. COMPASS 2018: 38:1-38:10 - [c34]Daniel Sheldon, Kevin Winner, Debora Sujono:
Learning in Integer Latent Variable Models with Nested Automatic Differentiation. ICML 2018: 4622-4630 - [c33]Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Inference for Exponential Families. NeurIPS 2018: 2924-2934 - [c32]Justin Domke, Daniel Sheldon:
Importance Weighting and Variational Inference. NeurIPS 2018: 4475-4484 - [c31]Rico Angell, Daniel Sheldon:
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes. NeurIPS 2018: 8998-9007 - [i11]Daniel Sheldon, Kevin Winner, Debora Sujono:
Learning in Integer Latent Variable Models with Nested Automatic Differentiation. CoRR abs/1806.03207 (2018) - [i10]Justin Domke, Daniel Sheldon:
Importance Weighting and Variational Inference. CoRR abs/1808.09034 (2018) - [i9]Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Inference for Exponential Families. CoRR abs/1809.02188 (2018) - 2017
- [j6]Christen H. Fleming, Daniel Sheldon, Eliezer Gurarie, William F. Fagan, Scott LaPoint, Justin M. Calabrese:
Kálmán filters for continuous-time movement models. Ecol. Informatics 40: 8-21 (2017) - [c30]XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein:
Robust Optimization for Tree-Structured Stochastic Network Design. AAAI 2017: 4545-4551 - [c29]Tao Sun, Daniel Sheldon, Brendan O'Connor:
A Probabilistic Approach for Learning with Label Proportions Applied to the US Presidential Election. ICDM 2017: 445-454 - [c28]Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau:
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. ICML 2017: 478-487 - [c27]Kevin Winner, Debora Sujono, Daniel Sheldon:
Exact Inference for Integer Latent-Variable Models. ICML 2017: 3761-3770 - [i8]Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau:
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models. CoRR abs/1706.04646 (2017) - 2016
- [c26]Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon:
Robust Decision Making for Stochastic Network Design. AAAI 2016: 3857-3863 - [c25]XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein:
Optimizing Resilience in Large Scale Networks. AAAI 2016: 3922-3928 - [c24]Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon:
Approximate Inference Using DC Programming For Collective Graphical Models. AISTATS 2016: 685-693 - [c23]Garrett Bernstein, Daniel Sheldon:
Consistently Estimating Markov Chains with Noisy Aggregate Data. AISTATS 2016: 1142-1150 - [c22]Aruni Roy Chowdhury, Daniel Sheldon, Subhransu Maji, Erik G. Learned-Miller:
Distinguishing Weather Phenomena from Bird Migration Patterns in Radar Imagery. CVPR Workshops 2016: 276-283 - [c21]Kevin Winner, Daniel Sheldon:
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models. NIPS 2016: 2640-2648 - [i7]Garrett Bernstein, Daniel Sheldon:
Consistently Estimating Markov Chains with Noisy Aggregate Data. CoRR abs/1604.04182 (2016) - [i6]XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein:
Robust Optimization for Tree-Structured Stochastic Network Design. CoRR abs/1612.00104 (2016) - 2015
- [j5]Shan Xue, Alan Fern, Daniel Sheldon:
Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization. J. Artif. Intell. Res. 52: 331-360 (2015) - [c20]Chia-Jung Lee, Qingyao Ai, W. Bruce Croft, Daniel Sheldon:
An Optimization Framework for Merging Multiple Result Lists. CIKM 2015: 303-312 - [c19]Tao Sun, Daniel Sheldon, Akshat Kumar:
Message Passing for Collective Graphical Models. ICML 2015: 853-861 - [c18]Kevin Winner, Garrett Bernstein, Daniel Sheldon:
Inference in a Partially Observed Queuing Model with Applications in Ecology. ICML 2015: 2512-2520 - [c17]XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein:
Fast Combinatorial Algorithm for Optimizing the Spread of Cascades. IJCAI 2015: 2655-2661 - [c16]Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum:
Bethe Projections for Non-Local Inference. UAI 2015: 892-901 - [e1]Bistra Dilkina, Stefano Ermon, Rebecca A. Hutchinson, Daniel Sheldon:
Computational Sustainability, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015. AAAI Technical Report WS-15-06, AAAI Press 2015, ISBN 978-1-57735-717-9 [contents] - [i5]Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum:
Bethe Projections for Non-Local Inference. CoRR abs/1503.01397 (2015) - 2014
- [j4]Andrew Farnsworth, Daniel Sheldon, Jeffrey Geevarghese, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling:
Reconstructing Velocities of Migrating Birds from Weather Radar - A Case Study in Computational Sustainability. AI Mag. 35(2): 31-48 (2014) - [c15]XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein:
Rounded Dynamic Programming for Tree-Structured Stochastic Network Design. AAAI 2014: 479-485 - [c14]Shan Xue, Alan Fern, Daniel Sheldon:
Dynamic Resource Allocation for Optimizing Population Diffusion. AISTATS 2014: 1033-1041 - [c13]Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich:
Gaussian Approximation of Collective Graphical Models. ICML 2014: 1602-1610 - [c12]XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein:
Stochastic Network Design in Bidirected Trees. NIPS 2014: 882-890 - [i4]Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich:
Gaussian Approximation of Collective Graphical Models. CoRR abs/1405.5156 (2014) - 2013
- [j3]Daniel Sheldon, Neal E. Young:
Hamming Approximation of NP Witnesses. Theory Comput. 9: 685-702 (2013) - [c11]Daniel Sheldon, Andrew Farnsworth, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling:
Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. AAAI 2013: 1334-1340 - [c10]Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich:
Approximate Inference in Collective Graphical Models. ICML (3) 2013: 1004-1012 - [c9]XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein:
Parameter Learning for Latent Network Diffusion. IJCAI 2013: 2923-2930 - [c8]Akshat Kumar, Daniel Sheldon, Biplav Srivastava:
Collective Diffusion Over Networks: Models and Inference. UAI 2013 - [i3]Akshat Kumar, Daniel Sheldon, Biplav Srivastava:
Collective Diffusion Over Networks: Models and Inference. CoRR abs/1309.6841 (2013) - 2012
- [j2]Thilanka Appuhamillage, Daniel Sheldon:
First Passage Time of Skew Brownian Motion. J. Appl. Probab. 49(3): 685-696 (2012) - [c7]Shan Xue, Alan Fern, Daniel Sheldon:
Scheduling Conservation Designs via Network Cascade Optimization. AAAI 2012: 391-397 - [c6]Thomas G. Dietterich, Ethan W. Dereszynski, Rebecca A. Hutchinson, Dan Sheldon:
Machine learning for computational sustainability. IGCC 2012: 1 - [i2]Daniel Sheldon, Bistra Dilkina, Adam N. Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla P. Gomes, David B. Shmoys, William Allen, Ole Amundsen, William Vaughan:
Maximizing the Spread of Cascades Using Network Design. CoRR abs/1203.3514 (2012) - [i1]Daniel Sheldon, Neal E. Young:
Hamming Approximation of NP Witnesses. CoRR abs/1208.0257 (2012) - 2011
- [c5]Daniel Sheldon, Thomas G. Dietterich:
Collective Graphical Models. NIPS 2011: 1161-1169 - [c4]Daniel Sheldon, Milad Shokouhi, Martin Szummer, Nick Craswell:
LambdaMerge: merging the results of query reformulations. WSDM 2011: 795-804 - 2010
- [b1]Dan Sheldon:
Manipulation of PageRank and Collective Hidden Markov Models. Cornell University, USA, 2010 - [c3]Daniel Sheldon, Bistra Dilkina, Adam N. Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla P. Gomes, David B. Shmoys, William Allen, Ole Amundsen, William Vaughan:
Maximizing the Spread of Cascades Using Network Design. UAI 2010: 517-526
2000 – 2009
- 2008
- [j1]John E. Hopcroft, Daniel Sheldon:
Manipulation-Resistant Reputations Using Hitting Time. Internet Math. 5(1): 71-90 (2008) - 2007
- [c2]Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Kozen:
Collective Inference on Markov Models for Modeling Bird Migration. NIPS 2007: 1321-1328 - [c1]John E. Hopcroft, Daniel Sheldon:
Manipulation-Resistant Reputations Using Hitting Time. WAW 2007: 68-81
Coauthor Index
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last updated on 2024-04-07 22:25 CEST by the dblp team
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